Capital is Back: From Labor to Capital in the Modern Economy

economy

python

Wealth-Income Ratios in Advanced Economies 1980-2023

Published

Aug 14, 2025

Keywords

wealth-income

Summary

The chart illustration the evolution of thel wealth-income ratio from 1980 to 2023 highlights the interplay between wealth accumulation and income generation over last five decades. It reveals a clear upward trend, reflecting the disproportionate growth of wealth relative to income, particularly in recent decades. This relationship is largely determined by the growth of the economy relative to the growth of capital. When capital grows at a faster rate than the economy, wealth concentrates disproportionately, amplifying disparities and altering the balance of economic power.

Code

# Libraries
# ===================================================
import os
import requests
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from matplotlib.offsetbox import OffsetImage, AnnotationBbox
from matplotlib.ticker import FuncFormatter
from io import BytesIO

# Extract Data (Countries)
# ===================================================
# Extract JSON to dataframe
url = 'https://raw.githubusercontent.com/guillemmaya92/world_map/main/Dim_Country.json'
response = requests.get(url)
data = response.json()
df = pd.DataFrame(data)
df = pd.DataFrame.from_dict(data, orient='index').reset_index()
df_countries = df.rename(columns={'index': 'ISO3'})

# Extract Data (WID)
# ===================================================
# Extract PARQUET to dataframe
url = "https://raw.githubusercontent.com/guillemmaya92/Analytics/master/Data/WID_Values.parquet"
df = pd.read_parquet(url, engine="pyarrow")

# Transform Data
# ===================================================
# Filter nulls and countries
df = df[df['wiratio'].notna()]
df = pd.merge(df, df_countries, left_on='country', right_on='ISO2', how='inner')

# Rename columns
df = df.rename(
        columns={
            'Country_Abr': 'country_name',
            'wiratio': 'beta'
        }
    )

# Filter countries have data post 1980
dfx = df.loc[df['year'] == 1980, 'country']
df = df[df['country'].isin(dfx)]
df = df[df['year'] >= 1980]
df = df[df['Analytical'] == 'Advanced Economies']

# Dataframe countries
dfc = df[df['country'].isin(['CN', 'US', 'DE', 'ES', 'JP', 'IN'])]

# Select columns and order
df = df[['year', 'country', 'country_name', 'beta']]

print(df)

# Visualization Data
# ===================================================
# Font Style
plt.rcParams.update({'font.family': 'sans-serif', 'font.sans-serif': ['Franklin Gothic'], 'font.size': 9})
sns.set(style="white", palette="muted")

# Create color dictionaire 
palette = {'CN': '#C00000', 'US': '#153D64', 'IN': '#E97132', 'DE': '#3C7D22', 'ES': '#ECB100', 'JP': "#782170"}

# Create line plots
fig, ax = plt.subplots(figsize=(8, 6))
sns.lineplot(data=df, x='year', y='beta', hue='country', linewidth=0.3, alpha=0.5, palette=["#A4A4A4"], legend=False, ax=ax)
sns.lineplot(data=dfc, x='year', y='beta', hue='country', linewidth=1.5, palette=palette, legend=False, ax=ax)

# Add title and subtitle
fig.add_artist(plt.Line2D([0.12, 0.12], [0.86, 0.97], linewidth=6, color='#203764', solid_capstyle='butt'))
plt.text(0.02, 1.14, f'Capital is back', fontsize=16, fontweight='bold', ha='left', transform=plt.gca().transAxes)
plt.text(0.02, 1.09, f'Wealth-Income Ratios in Advanced Economies 1980-2023', fontsize=11, color='#262626', ha='left', transform=plt.gca().transAxes)
plt.text(0.02, 1.05, f'(total wealth divided by annual income)', fontsize=9, color='#262626', ha='left', transform=plt.gca().transAxes)

# Custom plot
plt.xlabel('')
plt.ylabel('Wealth-Income Ratio (%)', fontsize=10, fontweight='bold')
formatter = FuncFormatter(lambda y, _: '{:,.0f}%'.format(y * 100))
plt.gca().yaxis.set_major_formatter(formatter)
plt.grid(axis='x', alpha=0.7, linestyle=':')
plt.ylim(0, 12)
plt.xlim(1980, 2026)
plt.xticks(range(1980, 2026, 10))
plt.tick_params(axis='both', labelsize=9)
plt.tight_layout()

# Delete spines
ax = plt.gca()
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['bottom'].set(color='gray', linewidth=1)
ax.spines['left'].set_linewidth(False)

# Add Data Source
plt.text(0, -0.12, 'Data Source:', 
    transform=plt.gca().transAxes, 
    fontsize=8,
    fontweight='bold',
    color='gray')
space = " " * 23
plt.text(0, -0.12, space + 'World Inequality Database', 
    transform=plt.gca().transAxes, 
    fontsize=8,
    color='gray')

# Add Data Source
plt.text(0, -0.15, 'Notes:', 
    transform=plt.gca().transAxes, 
    fontsize=8,
    fontweight='bold',
    color='gray')
space = " " * 12
plt.text(0, -0.15, space + 'Wealth-Income Ratio is the division of national wealth by national income.', 
    transform=plt.gca().transAxes, 
    fontsize=8,
    color='gray')

 # Add Year label
formatted_date = 2023
plt.text(1.07, 1.15, f'{formatted_date}',
    transform=plt.gca().transAxes,
    fontsize=20, ha='right', va='top',
    fontweight='bold', color='#D3D3D3')

# Define flags
flag_urls = {
    'CN': 'https://raw.githubusercontent.com/matahombres/CSS-Country-Flags-Rounded/master/flags/CN.png',
    'US': 'https://raw.githubusercontent.com/matahombres/CSS-Country-Flags-Rounded/master/flags/US.png',
    'ES': 'https://raw.githubusercontent.com/matahombres/CSS-Country-Flags-Rounded/master/flags/ES.png',
    'DE': 'https://raw.githubusercontent.com/matahombres/CSS-Country-Flags-Rounded/master/flags/DE.png',
    'JP': 'https://raw.githubusercontent.com/matahombres/CSS-Country-Flags-Rounded/master/flags/JP.png'
}
flags = {country: mpimg.imread(BytesIO(requests.get(url).content)) for country, url in flag_urls.items()}

# Custom offsets to move flags
y_offsets = {
    'ES': 0.1,
    'FR': -0.1,
    'US': -0.15,
    'CN': 0.0,
    'JP': 0.1
}

# Iterate over each country
for country in dfc['country'].unique():
    # Get country data
    country_data = dfc[dfc['country'] == country]
    last_point = country_data[country_data['year'] == country_data['year'].max()].iloc[0]
    x = last_point['year']
    y = last_point['beta'] + y_offsets.get(country, 0.0)
    country_name = last_point['country_name']
    value = last_point['beta'] * 100

    # Add flag
    flag_img = flags[country]
    imagebox = OffsetImage(flag_img, zoom=0.021, resample=True, alpha=0.8)
    ab = AnnotationBbox(imagebox, (x + 0.5, y), frameon=False, box_alignment=(0, 0.5))
    ax.add_artist(ab)

    # Add country name
    name_x = x + 2
    text_name = ax.text(name_x, y, country_name,
                        fontsize=8, va='center', ha='left', color=palette[country])

    # Canvas to measure large text, Get bbox name texto (px), Convert bbox to coordenates
    fig.canvas.draw()
    bbox_name = text_name.get_window_extent()
    inv = ax.transData.inverted()
    bbox_data = inv.transform([(bbox_name.x0, bbox_name.y0), (bbox_name.x1, bbox_name.y1)])
    width_data = bbox_data[1][0] - bbox_data[0][0]

    # Position beta after name
    label_x = name_x + width_data + 0.5
    label = f"({value:,.0f}%)"
    ax.text(label_x, y, label,
            fontsize=8, va='center', ha='left', fontweight='bold', color=palette[country])
    
# Adjust layout
plt.tight_layout()

# Save it...
download_folder = os.path.join(os.path.expanduser("~"), "Downloads")
filename = os.path.join(download_folder, f"FIG_WID_Beta_Evolution")
plt.savefig(filename, dpi=300, bbox_inches='tight')

# Show plot
plt.show()